In an era where financial markets are increasingly interconnected, one of the most critical gaps in trade surveillance remains unsolved: cross-market alerting.
Many firms have invested heavily in single-market surveillance frameworks, but these systems are still falling short when it comes to identifying manipulative strategies that span across instruments, venues, or asset classes, claims b-next.
This blind spot is drawing heightened attention from regulators, leaving financial institutions with little choice but to act. Advances in artificial intelligence (AI) and machine learning (ML) are now offering new ways to detect hidden, complex trading behaviours.
Cross-market alerting is the capability of surveillance platforms to detect potentially abusive or manipulative activity across multiple markets. Examples include trading in one venue to affect prices in another, coordinated strategies involving correlated instruments, layering or spoofing between futures and underlying equities, or wash trades conducted across different platforms. Traditional surveillance systems often miss these behaviours because they are limited by data silos or compliance teams’ inability to gain a unified view of activity.
The urgency for addressing this challenge has grown. Today’s market manipulation is rarely restricted to a single environment. Algorithmic trading, high-frequency strategies, and global market access have amplified the complexity of abuse. Regulators are responding. Enforcement actions increasingly cite failures to detect multi-market schemes, while frameworks such as the Market Abuse Regulation (MAR) in Europe and the SEC’s growing focus on surveillance technology in the US demonstrate that firms must now go beyond reactive compliance. To satisfy regulators, surveillance needs to reveal the intent behind behaviours, which requires connecting the dots across markets.
Yet implementing cross-market alerting is not without obstacles. Firms often contend with fragmented data architectures, with trading desks, asset classes, and regional operations running in separate systems. Even when data is available, differences in identifiers, formats, and time stamps make it hard to align meaningfully. Older surveillance technology compounds the issue, as these systems were not designed for multi-market monitoring. Furthermore, if alerts are poorly designed, firms risk overwhelming compliance teams with noise rather than providing clarity.
According to b-next, the way forward lies in scenario-based surveillance powered by flexible, normalised data layers and enhanced with AI-driven anomaly detection. Systems capable of ingesting data from multiple venues, reconciling related instruments, and applying advanced detection models can reveal hidden correlations. AI and ML are particularly suited to this task as they process vast volumes of trading data at speed, making it possible to uncover subtle manipulation patterns that traditional rule-based systems cannot. By shifting from reactive monitoring to proactive risk detection, firms can mitigate threats more effectively and protect market integrity.
Looking ahead, cross-market alerting is expected to become a standard requirement rather than an optional capability. As regulatory expectations intensify and manipulation techniques grow more sophisticated, financial firms will need to embrace AI and ML-enabled systems that provide comprehensive, integrated views of trading activity. This is not only a matter of avoiding penalties but of maintaining trust, ensuring fair markets, and staying ahead of increasingly complex abuse strategies. The question for firms is no longer whether to adopt cross-market alerting, but when and how successfully they will implement it.
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